Easy to use Audio Tagging in PyTorch

Related tags

Deep Learningaudiopytorchaudio-classificationsound-event-detectionaudio-taggingcnn14
Overview

Audio Classification, Tagging & Sound Event Detection in PyTorch

Progress:

  • Fine-tune on audio classification
  • Fine-tune on audio tagging
  • Fine-tune on sound event detection
  • Add tagging metrics
  • Add Tutorial
  • Add Augmentation Notebook
  • Add more schedulers
  • Add FSDKaggle2019 dataset
  • Add MTT dataset
  • Add DESED

Model Zoo

AudioSet Pretrained Models
Model Task mAP
(%)		 Sample Rate
(kHz)		 Window Length Num Mels Fmax Weights
CNN14 Tagging 43.1 32 1024 64 14k download
CNN14_16k Tagging 43.8 16 512 64 8k download
CNN14_DecisionLevelMax SED 38.5 32 1024 64 14k download

Note: These models will be used as a pretrained model in the fine-tuning tasks below. Check out audioset-tagging-cnn, if you want to train on AudioSet dataset.

Fine-tuned Classification Models
Model Dataset Accuracy
(%)		 Sample Rate
(kHz)		 Weights
CNN14 ESC50 (Fold-5) 95.75 32 download
CNN14 FSDKaggle2018 (test) 93.56 32 download
CNN14 SpeechCommandsv1 (val/test) 96.60/96.77 32 download
Fine-tuned Tagging Models
Model Dataset mAP(%) AUC d-prime Sample Rate
(kHz)		 Config Weights
CNN14 FSDKaggle2019 - - - 32 - -
Fine-tuned SED Models
Model Dataset F1 Sample Rate
(kHz)		 Config Weights
CNN14_DecisionLevelMax DESED - 32 - -

Supported Datasets

Dataset Task Classes Train Val Test Audio Length Audio Spec Size
ESC-50 Classification 50 2,000 5 folds - 5s 44.1kHz, mono 600MB
UrbanSound8k Classification 10 8,732 10 folds - <=4s Vary 5.6GB
FSDKaggle2018 Classification 41 9,473 - 1,600 300ms~30s 44.1kHz, mono 4.6GB
SpeechCommandsv1 Classification 30 51,088 6,798 6,835 <=1s 16kHz, mono 1.4GB
SpeechCommandsv2 Classification 35 84,843 9,981 11,005 <=1s 16kHz, mono 2.3GB
FSDKaggle2019* Tagging 80 4,970+19,815 - 4,481 300ms~30s 44.1kHz, mono 24GB
MTT* Tagging 50 19,000 - - - - 3GB
DESED* SED 10 - - - 10 - -

Notes: * datasets are not available yet. Classification dataset are treated as multi-class/single-label classification and tagging and sed datasets are treated as multi-label classification.

Dataset Structure (click to expand)

Download the dataset and prepare it into the following structure.

datasets
|__ ESC50
    |__ audio

|__ Urbansound8k
    |__ audio

|__ FSDKaggle2018
    |__ audio_train
    |__ audio_test
    |__ FSDKaggle2018.meta
        |__ train_post_competition.csv
        |__ test_post_competition_scoring_clips.csv

|__ SpeechCommandsv1/v2
    |__ bed
    |__ bird
    |__ ...
    |__ testing_list.txt
    |__ validation_list.txt

Augmentations (click to expand)

Currently, the following augmentations are supported. More will be added in the future. You can test the effects of augmentations with this notebook

WaveForm Augmentations:

  • MixUp
  • Background Noise
  • Gaussian Noise
  • Fade In/Out
  • Volume
  • CutMix

Spectrogram Augmentations:

  • Time Masking
  • Frequency Masking
  • Filter Augmentation

Usage

Requirements (click to expand)
  • python >= 3.6
  • pytorch >= 1.8.1
  • torchaudio >= 0.8.1

Other requirements can be installed with pip install -r requirements.txt.


Configuration (click to expand)
  • Create a configuration file in configs. Sample configuration for ESC50 dataset can be found here.
  • Copy the contents of this and then edit the fields you think if it is needed.
  • This configuration file is needed for all of training, evaluation and prediction scripts.

Training (click to expand)

To train with a single GPU:

$ python tools/train.py --cfg configs/CONFIG_FILE_NAME.yaml

To train with multiple gpus, set DDP field in config file to true and run as follows:

$ python -m torch.distributed.launch --nproc_per_node=2 --use_env tools/train.py --cfg configs/CONFIG_FILE_NAME.yaml

Evaluation (click to expand)

Make sure to set MODEL_PATH of the configuration file to your trained model directory.

$ python tools/val.py --cfg configs/CONFIG_FILE.yaml

Audio Classification/Tagging Inference
  • Set MODEL_PATH of the configuration file to your model's trained weights.
  • Change the dataset name in DATASET >> NAME as your trained model's dataset.
  • Set the testing audio file path in TEST >> FILE.
  • Run the following command.
$ python tools/infer.py --cfg configs/CONFIG_FILE.yaml

## for example
$ python tools/infer.py --cfg configs/audioset.yaml

You will get an output similar to this:

Class                     Confidence
----------------------  ------------
Speech                     0.897762
Telephone bell ringing     0.752206
Telephone                  0.219329
Inside, small room         0.20761
Music                      0.0770325

Sound Event Detection Inference
  • Set MODEL_PATH of the configuration file to your model's trained weights.
  • Change the dataset name in DATASET >> NAME as your trained model's dataset.
  • Set the testing audio file path in TEST >> FILE.
  • Run the following command.
$ python tools/sed_infer.py --cfg configs/CONFIG_FILE.yaml

## for example
$ python tools/sed_infer.py --cfg configs/audioset_sed.yaml

You will get an output similar to this:

Class                     Start    End
----------------------  -------  -----
Speech                      2.2    7
Telephone bell ringing      0      2.5

The following plot will also be shown, if you set PLOT to true:

sed_result


References (click to expand)

Citations (click to expand) 
@misc{kong2020panns,
      title={PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognition}, 
      author={Qiuqiang Kong and Yin Cao and Turab Iqbal and Yuxuan Wang and Wenwu Wang and Mark D. Plumbley},
      year={2020},
      eprint={1912.10211},
      archivePrefix={arXiv},
      primaryClass={cs.SD}
}

@misc{gong2021ast,
      title={AST: Audio Spectrogram Transformer}, 
      author={Yuan Gong and Yu-An Chung and James Glass},
      year={2021},
      eprint={2104.01778},
      archivePrefix={arXiv},
      primaryClass={cs.SD}
}

@misc{nam2021heavily,
      title={Heavily Augmented Sound Event Detection utilizing Weak Predictions}, 
      author={Hyeonuk Nam and Byeong-Yun Ko and Gyeong-Tae Lee and Seong-Hu Kim and Won-Ho Jung and Sang-Min Choi and Yong-Hwa Park},
      year={2021},
      eprint={2107.03649},
      archivePrefix={arXiv},
      primaryClass={eess.AS}
}
You might also like...
TorchMetrics is a collection of 25+ PyTorch metrics implementations and an easy-to-use API to create custom metrics.
TorchMetrics is a collection of 25+ PyTorch metrics implementations and an easy-to-use API to create custom metrics.

Machine learning metrics for distributed, scalable PyTorch applications.

Pytorch Lightning 1.2k Jan 6, 2023
TorchFlare is a simple, beginner-friendly, and easy-to-use PyTorch Framework train your models effortlessly.
TorchFlare is a simple, beginner-friendly, and easy-to-use PyTorch Framework train your models effortlessly.

TorchFlare TorchFlare is a simple, beginner-friendly and an easy-to-use PyTorch Framework train your models without much effort. It provides an almost

Atharva Phatak 85 Dec 26, 2022
A more easy-to-use implementation of KPConv based on PyTorch.

A more easy-to-use implementation of KPConv This repo contains a more easy-to-use implementation of KPConv based on PyTorch. Introduction KPConv is a

Zheng Qin 36 Dec 29, 2022
Use MATLAB to simulate the signal and extract features. Use PyTorch to build and train deep network to do spectrum sensing.

Deep-Learning-based-Spectrum-Sensing Use MATLAB to simulate the signal and extract features. Use PyTorch to build and train deep network to do spectru

null 10 Dec 14, 2022
Fast image augmentation library and easy to use wrapper around other libraries. Documentation: https://albumentations.ai/docs/ Paper about library: https://www.mdpi.com/2078-2489/11/2/125
Fast image augmentation library and easy to use wrapper around other libraries. Documentation: https://albumentations.ai/docs/ Paper about library: https://www.mdpi.com/2078-2489/11/2/125

Albumentations Albumentations is a Python library for image augmentation. Image augmentation is used in deep learning and computer vision tasks to inc

null 11.4k Jan 9, 2023
Fast, flexible and easy to use probabilistic modelling in Python.
Fast, flexible and easy to use probabilistic modelling in Python.

Please consider citing the JMLR-MLOSS Manuscript if you've used pomegranate in your academic work! pomegranate is a package for building probabilistic

Jacob Schreiber 3k Dec 29, 2022
High performance, easy-to-use, and scalable machine learning (ML) package, including linear model (LR), factorization machines (FM), and field-aware factorization machines (FFM) for Python and CLI interface.
High performance, easy-to-use, and scalable machine learning (ML) package, including linear model (LR), factorization machines (FM), and field-aware factorization machines (FFM) for Python and CLI interface.

What is xLearn? xLearn is a high performance, easy-to-use, and scalable machine learning package that contains linear model (LR), factorization machin

Chao Ma 3k Jan 3, 2023
High performance, easy-to-use, and scalable machine learning (ML) package, including linear model (LR), factorization machines (FM), and field-aware factorization machines (FFM) for Python and CLI interface.
High performance, easy-to-use, and scalable machine learning (ML) package, including linear model (LR), factorization machines (FM), and field-aware factorization machines (FFM) for Python and CLI interface.

What is xLearn? xLearn is a high performance, easy-to-use, and scalable machine learning package that contains linear model (LR), factorization machin

Chao Ma 2.8k Feb 12, 2021
A fast and easy to use, moddable, Python based Minecraft server!
A fast and easy to use, moddable, Python based Minecraft server!

PyMine PyMine - The fastest, easiest to use, Python-based Minecraft Server! Features Note: This list is not always up to date, and doesn't contain all

PyMine 144 Dec 30, 2022
Releases(v0.2.0)

  • v0.2.0(Aug 17, 2021)

    This release includes the following:

    • Fine-tuned on ESC50, FSDKaggle2018, SpeechCommandsv1
    • Add waveform augmentations
    • Add spectrogram augmentations
    • Add augmentation testing notebook
    • Add tagging metrics
    Source code(tar.gz)
    Source code(zip)

  • v0.1.0(Aug 13, 2021)

Owner
sithu3
AI Developer
sithu3
GitHub Repository
An integration of several popular automatic augmentation methods, including OHL (Online Hyper-Parameter Learning for Auto-Augmentation Strategy) and AWS (Improving Auto Augment via Augmentation Wise Weight Sharing) by Sensetime Research.

An integration of several popular automatic augmentation methods, including OHL (Online Hyper-Parameter Learning for Auto-Augmentation Strategy) and AWS (Improving Auto Augment via Augmentation Wise

45 Dec 08, 2022
A set of tools for Namebase and HNS

HNS-TOOLS A set of tools for Namebase and HNS To install: pip install -r requirements.txt To run: py main.py My Namebase referral code: http://namebas

RunDavidMC 7 Apr 08, 2022
BED: A Real-Time Object Detection System for Edge Devices

BED: A Real-Time Object Detection System for Edge Devices About this project Thi

Data Analytics Lab at Texas A&M University 44 Nov 18, 2022
Project code for weakly supervised 3D object detectors using wide-baseline multi-view traffic camera data: WIBAM.

WIBAM (Work in progress) Weakly Supervised Training of Monocular 3D Object Detectors Using Wide Baseline Multi-view Traffic Camera Data 3D object dete

Matthew Howe 10 Aug 24, 2022
PyTorch implementation of Glow

glow-pytorch PyTorch implementation of Glow, Generative Flow with Invertible 1x1 Convolutions (https://arxiv.org/abs/1807.03039) Usage: python train.p

Kim Seonghyeon 433 Dec 27, 2022
P-Tuning v2: Prompt Tuning Can Be Comparable to Finetuning Universally Across Scales and Tasks

P-tuning v2 P-Tuning v2: Prompt Tuning Can Be Comparable to Finetuning Universally Across Scales and Tasks An optimized prompt tuning strategy achievi

THUDM 540 Dec 30, 2022
A supplementary code for Editable Neural Networks, an ICLR 2020 submission.

Editable neural networks A supplementary code for Editable Neural Networks, an ICLR 2020 submission by Anton Sinitsin, Vsevolod Plokhotnyuk, Dmitry Py

Anton Sinitsin 32 Nov 29, 2022
DROPO: Sim-to-Real Transfer with Offline Domain Randomization

DROPO: Sim-to-Real Transfer with Offline Domain Randomization Gabriele Tiboni, Karol Arndt, Ville Kyrki. This repository contains the code for the pap

Gabriele Tiboni 8 Dec 19, 2022
Pytorch implementation of Hinton's Dynamic Routing Between Capsules

pytorch-capsule A Pytorch implementation of Hinton's "Dynamic Routing Between Capsules". https://arxiv.org/pdf/1710.09829.pdf Thanks to @naturomics fo

Tim Omernick 625 Oct 27, 2022
Repositório criado para abrigar os notebooks com a listas de exercícios propostos pelo professor Gustavo Guanabara do canal Curso em Vídeo do YouTube durante o Curso de Python 3

Curso em Vídeo - Exercícios de Python 3 Sobre o repositório Este repositório contém os notebooks com a listas de exercícios propostos pelo professor G

João Pedro Pereira 9 Oct 15, 2022
Change is Everywhere: Single-Temporal Supervised Object Change Detection in Remote Sensing Imagery (ICCV 2021)

Change is Everywhere Single-Temporal Supervised Object Change Detection in Remote Sensing Imagery by Zhuo Zheng, Ailong Ma, Liangpei Zhang and Yanfei

Zhuo Zheng 125 Dec 13, 2022
TACTO: A Fast, Flexible and Open-source Simulator for High-Resolution Vision-based Tactile Sensors

TACTO: A Fast, Flexible and Open-source Simulator for High-Resolution Vision-based Tactile Sensors This package provides a simulator for vision-based

Facebook Research 255 Dec 27, 2022
Trying to understand alias-free-gan.

alias-free-gan-explanation Trying to understand alias-free-gan in my own way. [Chinese Version 中文版本] CC-BY-4.0 License. Tzu-Heng Lin motivation of thi

Tzu-Heng Lin 12 Mar 17, 2022
PyTorch implementation of the implicit Q-learning algorithm (IQL)

Implicit-Q-Learning (IQL) PyTorch implementation of the implicit Q-learning algorithm IQL (Paper) Currently only implemented for online learning. Offl

Sebastian Dittert 27 Dec 30, 2022
Developing your First ML Workflow of the AWS Machine Learning Engineer Nanodegree Program

Exercises and project documentation for the 3. Developing your First ML Workflow of the AWS Machine Learning Engineer Nanodegree Program

Simona Mircheva 1 Jan 13, 2022
Code for Two-stage Identifier: "Locate and Label: A Two-stage Identifier for Nested Named Entity Recognition"

Code for Two-stage Identifier: "Locate and Label: A Two-stage Identifier for Nested Named Entity Recognition", accepted at ACL 2021. For details of the model and experiments, please see our paper.

tricktreat 87 Dec 16, 2022
The description of FMFCC-A (audio track of FMFCC) dataset and Challenge resluts.

FMFCC-A This project is the description of FMFCC-A (audio track of FMFCC) dataset and Challenge resluts. The FMFCC-A dataset is shared through BaiduCl

18 Dec 24, 2022
Fast and simple implementation of RL algorithms, designed to run fully on GPU.

RSL RL Fast and simple implementation of RL algorithms, designed to run fully on GPU. This code is an evolution of rl-pytorch provided with NVIDIA's I

Robotic Systems Lab - Legged Robotics at ETH Zürich 68 Dec 29, 2022
Facilitating Database Tuning with Hyper-ParameterOptimization: A Comprehensive Experimental Evaluation

A Comprehensive Experimental Evaluation for Database Configuration Tuning This is the source code to the paper "Facilitating Database Tuning with Hype

DAIR Lab 9 Oct 29, 2022
MoViNets PyTorch implementation: Mobile Video Networks for Efficient Video Recognition;

MoViNet-pytorch Pytorch unofficial implementation of MoViNets: Mobile Video Networks for Efficient Video Recognition. Authors: Dan Kondratyuk, Liangzh

189 Dec 20, 2022